WO2024000018A1 - Essai sur site et de confirmation - Google Patents

Essai sur site et de confirmation Download PDF

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Publication number
WO2024000018A1
WO2024000018A1 PCT/AU2023/050580 AU2023050580W WO2024000018A1 WO 2024000018 A1 WO2024000018 A1 WO 2024000018A1 AU 2023050580 W AU2023050580 W AU 2023050580W WO 2024000018 A1 WO2024000018 A1 WO 2024000018A1
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Prior art keywords
sample
image data
protein
sampling device
visual indication
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PCT/AU2023/050580
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English (en)
Inventor
Joost Laurus Dinant NELIS
James Broadbent
Sarah MODDEJONGEN
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Commonwealth Scientific And Industrial Research Organisation
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Priority claimed from AU2022901848A external-priority patent/AU2022901848A0/en
Application filed by Commonwealth Scientific And Industrial Research Organisation filed Critical Commonwealth Scientific And Industrial Research Organisation
Publication of WO2024000018A1 publication Critical patent/WO2024000018A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6818—Sequencing of polypeptides
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
    • G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
    • G01N21/78—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator producing a change of colour
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G01N30/7233—Mass spectrometers interfaced to liquid or supercritical fluid chromatograph
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
    • G01N33/54366—Apparatus specially adapted for solid-phase testing
    • G01N33/54386—Analytical elements
    • G01N33/54387—Immunochromatographic test strips
    • G01N33/54388—Immunochromatographic test strips based on lateral flow
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N2021/1748—Comparative step being essential in the method
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N2021/1765—Method using an image detector and processing of image signal
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
    • G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
    • G01N2021/7756—Sensor type
    • G01N2021/7759—Dipstick; Test strip
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
    • G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
    • G01N2021/7769—Measurement method of reaction-produced change in sensor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
    • G01N2030/8831—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials involving peptides or proteins
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N35/00—Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/00 - G01N33/00; Handling materials therefor
    • G01N35/00584—Control arrangements for automatic analysers
    • G01N35/00594—Quality control, including calibration or testing of components of the analyser
    • G01N35/00693—Calibration
    • G01N2035/00702—Curve-fitting; Parameter matching; Calibration constants
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N35/00—Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/00 - G01N33/00; Handling materials therefor
    • G01N35/00584—Control arrangements for automatic analysers
    • G01N35/00722—Communications; Identification
    • G01N35/00732—Identification of carriers, materials or components in automatic analysers
    • G01N2035/00742—Type of codes

Definitions

  • This disclosure relates to detecting and quantifying a protein in a sample.
  • a method for detecting a protein in a sample comprising: receiving a sampling device containing the sample, the sampling device comprising an absorbent material with a test area configured to provide a visual indication of the protein in the sample; capturing image data of the sampling device containing the sample, the image data capturing the visual indication; extracting the sample from the sampling device; and detecting the protein using liquid chromatography mass spectrometry (LC- MS) analysis of the extracted sample to confirm detection of the protein as visually indicated by the image data.
  • LC- MS liquid chromatography mass spectrometry
  • sampling device can be used for both the collection and extraction of a sample containing a protein, as the sampling devices can be produced at relatively low costs and can be easily used.
  • the sampling devices can also be used to collect and transport the sample from first location to a second location that is remote from the first location, for LC-MS analysis.
  • extracting the sample from the sampling device comprises chemically extracting the sample to produce an extracted sample using an extraction solution and a first spin filter to produce a supernatant containing the extracted sample.
  • chemically extracting the sample from the sampling device comprises transferring the supernatant to a second spin filter and applying a digestion solution to obtain the extracted sample from the supernatant.
  • the method further comprises detecting a false negative or a false positive by the sampling device using the LC-MS analysis to detect the protein below a limit of detection of the sampling device.
  • the sampling device is provided with a machine readable unique identifier and the machine readable unique identifier comprises one or more of: a barcode; a QR code; and a serial number.
  • the protein is part of: an allergen; an antibody; an antigen; a toxin; or a pathogen.
  • the sampling device comprises a lateral flow device.
  • the sampling device comprises a sampling pad configured to receive the sample and the method comprises extracting the sample from the sampling pad for the LC-MS analysis.
  • the sample is collected from a first location using the sampling device and the LC-MS analysis is performed at a second location remote from the first location.
  • the method further comprises: calculating an intensity ratio from the image data by comparing a first intensity of the test area of the absorbent material with a second intensity of an area of the absorbent material other than the test area; and calculating a quantity of the protein present in the sample based on the intensity ratio.
  • capturing the image data comprises capturing the image data with a camera integrally contained in a communication device.
  • the method further comprises creating calibration curves using multiple calibration samples, wherein the calibration samples contain known concentrations of the protein.
  • one of the calibration curves is stored in the communication device for use with sampling devices of multiple pre-defined types.
  • the calibration curves are created and stored on a server device.
  • the method further comprises correcting the image data using a correction ratio calculated based on an average of the first intensity and an average of the second intensity.
  • correcting the image data comprises detecting the visual indication of the protein in the sample in the image data to thereby detect the protein in the sample from the image data.
  • the image data further comprises data indicative of one or more of: a GPS location; a time stamp; and the machine readable unique identifier uniquely associated with the sampling device.
  • the image data is in a RAW image format.
  • the method further comprises determining a quantity of the protein in the sample using the LC-MS analysis.
  • the sampling device provides an internal standard
  • the method comprises using the internal standard in the LC-MS analysis to determine the quantity of the protein in the sample using the LC-MS analysis.
  • the internal standard is a heavy carbon isotope standard.
  • the method further comprises updating the calibration curves at the intensity ratio in the image data with the quantity of the sample determined using the LC-MS analysis.
  • the method further comprises determining peptide sequence information of the protein using the LC-MS analysis.
  • the method further comprises detecting the presence of a second protein using the LC-MS analysis to verify a purported origin of the sample.
  • a system for detecting a protein in a sample comprising: a sampling device configured to receive the sample, the sampling device comprising an absorbent material with a test area configured to provide a visual indication of the protein in the sample; and a liquid chromatography mass spectrometry (LC-MS) system configured to perform LC-MS analysis to detect the protein in the sample extracted from the sampling device, wherein the LC-MS analysis confirms detection of the protein as visually indicated by the image data.
  • LC-MS liquid chromatography mass spectrometry
  • a method for quantifying a protein in a sample comprising: capturing image data of a sampling device containing the sample with a camera of a communication device, wherein the sampling device comprises an absorbent material and is configured to provide a visual indication of the protein in the sample; the sampling device is provided with a machine readable unique identifier that is uniquely associated with the sampling device; the sampling device provides an internal standard; and the image data comprises data indicative of the machine readable unique identifier to identify the sampling device from the image data, and the visual indication of the protein in the sample; calculating, by the communication device, a quantity of the protein in the sample based on the visual indication in the image data; sending the image data to a server device; performing, by a server device, the steps of: receiving the image data; confirming the identity of the sampling device using the data indicative of the machine readable unique identifier in the image data extracting the sample from the sampling device chemically using one or more spin filters; and in response to confirming the identity of the sampling device, confirming the quantity of
  • a system for quantifying a protein in a sample comprising: a sampling device, wherein the sampling device comprises an absorbent material and is configured to provide a visual indication of the protein in the sample, the sampling device is provided with a machine readable unique identifier that is uniquely associated with the sampling device, and the sampling device provides an internal standard; a communication device comprising a processor, wherein the processor is configured to: capture image data of a sampling device containing the sample with a camera of the communication device, wherein the image data comprises data indicative of the machine readable unique identifier to identify the sampling device from the image data, and the visual indication of the protein in the sample, calculate a quantity of the protein in the sample based on the visual indication in the image data, and send the image data to a server device; the server device configured to: receive the image data from the communication device, and confirm the identity of the sampling device using the data indicative of the machine readable unique identifier in the image data; and a liquid chromatography mass spectrometry (LC-MS) system configured
  • a method for detecting a protein in a sample comprising: capturing image data of a sampling device containing the sample with a camera of a communication device, wherein the sampling device comprises an absorbent material and is configured to provide a visual indication of the protein in the sample; the sampling device is provided with a machine readable unique identifier that is uniquely associated with the sampling device; the sampling device provides an internal standard; and the image data comprises data indicative of the machine readable unique identifier to identify the sampling device from the image data, and the visual indication of the protein in the sample; processing, by the communication device, the image data to detect the visual indication in the image data to thereby detect the protein in the sample using the visual indication in the image data; sending the image data to a server device; performing, by a server device, the steps of: receiving the image data; confirming the identity of the sampling device using the data indicative of the machine readable unique identifier in the image data; extracting the sample from the sampling device chemically using one or more spin filters; and in response to confirming the identity
  • the visual indication may also provide a preliminary detection of the protein in the sample. The preliminary result is useful in situations where a quick result is needed, which can later be confirmed and quantified by the LC- MS analysis.
  • a system for detecting a protein in a sample comprising: a sampling device, wherein the sampling device comprises an absorbent material and is configured to provide a visual indication of the protein in the sample, the sampling device is provided with a machine readable unique identifier that is uniquely associated with the sampling device, and the sampling device provides an internal standard; a communication device comprising a processor, wherein the processor is configured to: capture image data of a sampling device containing the sample with a camera of the communication device, wherein the image data comprises data indicative of the machine readable unique identifier to identify the sampling device from the image data, and the visual indication of the protein in the sample, process the image data to detect the visual indication in the image data to thereby detect the protein in the sample using the visual indication in the image data, and send the image data to a server device; the server device configured to: receive the image data from the communication device, and confirm the identity of the sampling device using the data indicative of the machine readable unique identifier in the image data; and a liquid chromatography mass
  • Fig. 1 illustrates a method for detecting a protein in a sample.
  • Fig. 2a illustrates an absorbent material of a lateral flow assay before application of a sample.
  • Fig. 2b illustrates the absorbent material after the sample has been applied to the sampling pad.
  • Fig. 2c illustrates the absorbent material after the lateral flow process.
  • Fig. 3a illustrates a method for determining a quantity of a protein in a sample.
  • Fig. 3b illustrates a method for detecting a protein in a sample.
  • FIG. 4 illustrates an example system for performing one or more of the methods described herein.
  • Fig. 5 illustrates different calibration curves for relative gold nanoparticle (GNP) concentration created using image data from a communication device.
  • Fig. 6 illustrates calibration curves of GNP concentration and intensity in colour channels of JPEG and RAW images captured using Android and iPhone mobile phone models at 1/60 shutter time.
  • Fig. 7 illustrates smartphone based quantification of commercial lateral flow assays.
  • Fig. 8 illustrates a smartphone application for colour quantification.
  • Fig. 9 illustrates optimisation of the LC-MS extraction protocol.
  • Fig. 10 illustrates the direct quantification of allergenic peptides from LFAs by LC-MS.
  • Targeted proteomic approaches show great promise for laboratory-based quantification of allergens, because such methods do not suffer from cross-reactivity issues, have excellent multiplexing potential, and, can be used as confirmatory analysis to screening tests, which further improves food safety.
  • Such confirmatory analysis include the use of liquid chromatography mass spectrometry (LC-MS), which combines the physical separation capabilities of liquid chromatography (or high-performance liquid chromatography) with the mass analysis capabilities of mass spectrometry (MS). While liquid chromatography (LC) separates mixtures with multiple components, mass spectrometry provides spectrometric information that may help to identify (or confirm the suspected identity of) each separated component.
  • LC-MS liquid chromatography mass spectrometry
  • MS mass analysis capabilities
  • LC-MS approaches tend to be time consuming and are performed in a laboratory, which may be located remotely from a food production facility, for example.
  • LFA Lateral flow assays
  • LFAs are valuable on-site tests. These assays eliminate the need for specialist equipment and expertise required for traditional laboratory methods. LFAs can enable food manufacturers to test individual batches for crosscontamination. LFAs can empower consumers to monitor the safety of their own food as well and democratise analytical chemistry protocol execution. LFAs are primarily qualitative, reporting the presence or absence of a target analyte. A crucial limitation of LFA interpretation is the reliance on visual inspection, such as naked-eye readings. Variable environmental conditions, such as low levels of ambient light and glare, on top of perceptual differences between users make these assays highly susceptible to misinterpretation. This effect is compounded if a user has impaired colour perception or is longsighted.
  • LC-MS are developed for the detection of allergens from raw material, therefore it is difficult to confirm LFA results directly by LC-MS since LC-MS would use a new extraction from novel raw material. Developing an LC-MS approach that enables direct confirmation of an LFA result would thus be beneficial for LFA assay developers (to confirm assay selectivity) and LFA users and regulatory bodies alike (to assure that the assay was performed correctly and confirm LFA test results in a disputed case result, such as a true positive, a false negative or a false positive result).
  • Smartphone-based quantification of the LFA results is also appealing because smartphones remove costs associated with additional hardware, such as the digital readers that are specific for a particular LFA. Moreover, smartphones have the capacity to enable real-time geotagging of test locations, secure data storage and management via the cloud and rapid communication to stakeholders. However, smartphone-based LFA quantification has proven challenging with model-dependent spectral sensitivity and large inter-phone variations observed.
  • This disclosure proposes a new pipeline and methods for compliance testing using smartphone-based on-site screening and rapid confirmatory analysis.
  • this disclosure is directed towards the detection and quantification of a protein in a sample from a sampling device, such as a LFA.
  • a particular protein or the constitutes of a protein, as known as ‘peptides’
  • peptides a protein in the sample
  • the disclosed methods are directed towards the detection and quantification of a protein to determine the presence of an allergen in a sample, the disclosed methods are applicable for different types of proteins.
  • the protein may be part of an allergen, an antibody, an antigen, a toxin or a pathogen. Therefore the methods in this disclosure have many applications, and are not simply limited to the food industry.
  • a preliminary detection of the protein in a sample is performed by collecting the sample using a sampling device, such as a LFA, at an onsite location.
  • the sampling device gives a visual indication of the presence of the protein in the sample.
  • the preliminary detection of the protein in the sample may also be additionally confirmed by capturing an image of the sampling device using a communication device, such as a smartphone.
  • the communication device may be configured to determine the presence of the protein in the sample using the image captured by the device to analyse the visual test line of the sampling device. Additionally, the communication device may be configured to determine a preliminary quantity of the protein in the sample using the image captured by the device.
  • the sampling device containing the sample can then be sent to another facility or location for analysis of the sample contained in the sampling device using LC-MS.
  • the sampling device serves as a means of transporting the sample, as the sample can be directly extracted from the sampling device.
  • LC-MS of the sample determines the presence and quantity of the protein in the sample, which can be used to confirm the preliminary results of the sampling device at the on-site location.
  • Smartphone-based detection is achieved with comparable sensitivity as when performed with the naked eye for all target assays. Quantification was achieved with varying degrees of prediction error (ranging between 15% and 50% depending on the assay). This is a considerable improvement to the art where only a present or absent determination can be made.
  • the LC-MS methods show very promising results with detection limits well below the described detection limits, or limit of detection, of the screening tests. Moreover, additional peptides can be detected in the LC-MS method to verify the test was conducted in the correct sample environment (or matrix) and, the LC-MS method can be used to verify if a test was conducted correctly and may be used as a valuable control method.
  • the disclosed methods may facilitate acquiring export compliance documentation regarding allergens, pathogens or protein toxins whereby the smartphone -based screening can be used to generate swift compliance documentation and the LC-MS check can be used to enable occasional controls to check that LFA tests were conducted correctly (by detecting typical peptides found in the matrix as well as the target compound).
  • This can be of value to food exporters/producers and clearly aligns with the Trusted Agrifood Exports (TAE) missions’ goal to increase market access by developing new approaches to meeting the regulatory requirements of trading partners.
  • TEE Trusted Agrifood Exports
  • this disclosure uses DNG smartphone images and ratio metric ambient light correction for colour quantification of LFAs via a smartphone followed by subsequent LC-MS analysis directly on extracts obtained from the LFAs.
  • Gold nanoparticles GNPs
  • RGB colour channels and grey scale from JPEG images were compared with raw intensity values from DNG images under various lighting conditions.
  • the best system was used for the quantification of commercial allergen LFA test strips for gluten (Gluten Protein Rapid Kit, 3M) and peanut (AgraStrip®, Romer; Peanut Protein Rapid Kit, 3M) allergen and the method performance was thoroughly validated.
  • Fig. 1 illustrates a method 100 for detecting a protein in a sample.
  • method 100 may be performed by a single person, such as a food safety technician or a laboratory technician, or may be performed by multiple people.
  • method 100 comprises collecting 101 the sample using a sampling device.
  • the sampling device may comprise a lateral flow device, such as a lateral flow assay (LFA), lateral flow immunoassay (LFI), rapid antigen test (RAT), or a dipstick.
  • LFA lateral flow assay
  • LFI lateral flow immunoassay
  • RAT rapid antigen test
  • the sampling device may be any device configured to collect the sample for LC-MS analysis.
  • the sampling device may also be any commercially available lateral flow device, or may be a lateral flow device that is developed in-house or by a research facility.
  • the sampling device may comprise an absorbent material, that absorbs the sample and thereby, collects 101 the sample for detection of the protein.
  • the absorbent material may be a paper material, made of cellulose, as an example, noting that other absorbent materials, including synthetic materials, are equally usable.
  • the sampling device may further comprise a housing that holds the absorbent material.
  • Fig. 2a illustrates an absorbent material 200 of a lateral flow assay before application of a sample 201.
  • the sample 201 contains the protein 202, which is to be detected using the absorbent material 200 by performing method 100. Collecting the sample 101 occurs by bringing the sample 201 in contact with the absorbent material 200 at the sampling pad 203.
  • the sampling device may comprise a sampling pad 203 configured to receive the sample 201.
  • the absorbent material 200 further comprises a conjugate pad 204, which contains multiple labelled conjugates 205.
  • Each labelled conjugate 205 comprises a label (represented by the small black circles), such as a gold nanoparticle, for example, as well as a binding body (represented by the Y- shaped object).
  • the label of the labelled conjugate 205 produces a colour that can be visually seen by a human, which is used to provide a visual indication of the detection of the protein in the sample.
  • the label may be a fluorescent or magnetic labelled particle, that would use an electronic reader to assess the result.
  • the binding body is a conjugate molecule to the protein 202 being detected, in the sense that, the binding body may bind to the protein 202. In an example, if the protein 202 is part of an antigen, the binding body would the conjugate antibody.
  • the absorbent material 200 further comprises a test area 206 and a control area
  • the test area 206 contains the same or similar binding bodies 208 to the labelled conjugates 205 and therefore, are able to bind to the protein 202. As the labelled conjugates 205 attach, with the protein 202, to the binding bodies 208 of the test area
  • the labels of the labelled conjugates 205 provide a visual indication of the presence of the protein 202 in the sample 201.
  • the control area 207 contains binding bodies 209 that bind to the binding body of the labelled conjugates 205, rather than the protein 202. As the labelled conjugates 205 attach to the binding bodies 209 of the control area 207, the labels of the labelled conjugates 205 provide a visual indication that the sampling device is working correctly.
  • the absorbent material 200 also further comprises a wicking pad 210, which is an absorbent pad designed to draw the sample 201 from the sampling pad 203 and initiate flow of the sample 201 through the absorbent material 200.
  • the flow of the sample 201 through the absorbent material 200 terminates at the wicking pad 210, where the wicking pad 210 also acts as a waste collection site.
  • the absorbent material 200 further comprises a blank area 211, which only comprises the absorbent material.
  • the blank area 211 comprises multiple areas: one area defined between the conjugate pad 204 and the test area 206, one area defined between the test area 206 and the control 207, and one area defined between the control area 207 and the wicking pad 210.
  • the blank area 211 is generally the region of the absorbent material 200 where no binding occurs and is generally white to a user who visually inspects the absorbent material 200.
  • the sampling pad 203, conjugate pad 204 and the wicking pad 210 may each be a separate or individual pad that attaches to the absorbent material 200.
  • the absorbent material 200 may be fixed to an inert backing material.
  • the sampling device may comprise housing configured to hold the absorbent material 200.
  • the housing may be configured with a window, such that a user may visually see the test area 206 and the control area 207 through the window, without needing to remove the absorbent material 200 from the housing.
  • the window may also show all or part of the blank area 211.
  • the housing may also comprise an opening over the sampling pad 203, that may be used to receive the sample 201 using the sampling pad 203 without removing the absorbent material 200 from the housing.
  • Figs. 2a-2c only three protein molecules, labelled conjugates, and binding bodies in the test and control area are shown for illustration purposes only and any number of these molecule may be present in reality. However, in general, there are more labelled conjugates 205 than protein 202 molecules. The excess of unbound labelled conjugates 205 are used to bind to the binding bodies 209 of the control area 207, thereby providing a visual indication to the user that the sampling device is working correctly. It is noted that the presence of a visual indication in the test area 206 and the absence of a visual indication in the control area 207, indicates that the sampling has not worked correctly.
  • the labelled conjugate 205 may only bind to the binding bodies 208 of the test area 206 and not to the protein 202 or the binding bodies 209 of the control area 207. Therefore, this would indicative that lateral flow of the sampling device was not performed correctly.
  • Fig. 2b illustrates the absorbent material 200 after the sample 201 has been applied to the sampling pad 203. After the sample 201 has been applied to the sampling pad 203, the sample 201 flows through the absorbent material 200, as indicated by the flow direction 221. The flow of the sample 201 through the absorbent material 200 may also be referred to as lateral flow. As such, Fig. 2b illustrates the absorbent material 200 during the lateral flow of the sample 201 through the absorbent material 200. As the sample 201 flows through the absorbent material 200, the protein 202 molecules in the sample 201 move from the sampling pad 203 through the conjugate pad 204, which allow the proteins 202 to bind the labelled conjugates 205, which then form bound conjugates 222.
  • the bound conjugates 222 still have part of the protein exposed to allow further binding to occur. It should be noted that not all protein 202 molecules may bind with the labelled conjugates 205. Some of the protein 202 molecules may remain in the sampling pad 203 or any other area of the absorbent material during the lateral flow process. It should be further noted that the entire sample 201 may not flow across the entire absorbent material 200. In most examples, a sufficient amount of the sample 201 may remain in the sampling pad 203 after application. Thus the sample 201 may be extracted from the sampling pad 203 at a later stage.
  • Fig. 2c illustrates the absorbent material 200 after the lateral flow process.
  • the bound conjugates 222 move through the absorbent material and further bind to the binding bodies 208 on the test area 206. As part of the protein is still exposed in the bound conjugates 222, this allows it to further bind 241 with the binding bodies on the test area.
  • the label in the conjugate gives a visual indication of the presence of the conjugate on the test area. As such, the test area 206 gives a visual indication of the presence of the protein 202 in the sample 201.
  • conjugates that do not bind to a protein 202 flow pass the test area 206 and are able to bind to the binding bodies in the control area 242. These conjugates may be referred to as control conjugates.
  • control conjugates may be referred to as control conjugates.
  • the binding of the conjugates at the control area 242 gives a visual indication of this binding. The visual indication of this binding at the control area 242 indicates that the test, as performed by the absorbent material of the sampling device, was performed correctly.
  • Method 100 then comprises extracting 102 the sample 201 from the sampling device.
  • the sample 201 is extracted from the sampling pad 203 of the absorbent material, which forms part of the sampling device.
  • the sampling device comprises a sampling pad configured to receive the sample and the method 100 comprises extracting 102 the sample form the sampling pad.
  • method 100 comprises detecting 103 the protein in the sample using liquid chromatography mass spectrometry (LC-MS).
  • LC-MS liquid chromatography mass spectrometry
  • the sample is extracted from the wicking pad 210.
  • the sampling device may be configured to provide a visual indication of the protein in the sample
  • the detection of the protein by the visual indication may be confirmed using the LC-MS analysis.
  • detecting the protein using the LC-MS analysis comprises confirming detection of the protein as visually indicated by the LFA sampling device.
  • detecting the protein using liquid chromatography mass spectrometry (LC-MS) analysis of the extracted sample can be used to confirm detection of the protein as visually indicated by the image data.
  • LC-MS liquid chromatography mass spectrometry
  • Fig. 3a illustrates a method 300 for determining a quantity of a protein in a sample.
  • Method 300 may be performed in conjunction with or instead of method 100.
  • Method 300 first comprises capturing 301 image data of a sampling device containing the sample with a camera of a communication device.
  • the communication device may comprise a processor as well as additional modules, that are configured to capture the image data, store the image data on the device and communicate the image data.
  • the processor of the communication device may be additionally configured to perform one or more of the methods described herein.
  • the communication device may be any device configured with a camera or image sensor that may capture the image data and communicate the image data to another communication device or to a remote server via the internet or any other means of communication.
  • Examples of a communication device include, but are not limited to, a smartphone, tablet or a digital reader with camera and communication functionally.
  • the image data may comprise data indicative of the sampling device and/or the sample.
  • the image data may also comprise additional data including, but not limited to, a GPS location of where the image data was captured and a time stamp including the time and date of when the image data was captured.
  • the sampling device may have a machine readable unique identifier of the sampling device.
  • This machine readable unique identifier may comprise one or more of: a barcode, a QR code or a serial number, that uniquely identifies the sampling device and distinguishes the sampling device from other sampling devices.
  • the machine readable unique identifier may have encoded information that, when decoded, provides information relating to the manufacturer of the sampling device or the protein that the sampling device is designed to detect.
  • the image data captured by the communication device may comprise this machine readable unique identifier, such as a photo of the QR code.
  • the sampling device comprises an absorbent material and is configured to provide a visual indication of the protein in the sample.
  • the sampling device may also provide an internal standard, which is used in subsequent steps involving LC-MS analysis.
  • the internal standard may be a heavy carbon isotope standard, such as, for example, a polypeptide or protein containing 13C stable isotopes.
  • the internal standard is used in LC-MS analysis to provide a reference standard in the analysis. As the mass-to-charge ratio for the internal standard is known along with the concentration of the internal standard, the internal standard may be used to determine the quantity of the protein in the sample during the LC-MS analysis.
  • This may be performed by comparing the peaks of the internal standard and the peaks of the peptides in the LC-MS spectrum as the location and concentration of the internal standard peak is known beforehand. Rather than comparing peaks, this may be performed by comparing full width at half maximums (FWHMs) or by integrating the area under the peaks and comparing the results of the integration.
  • the comparison to determine the quantity of the protein in the sample may be performed by software, executed by a processor, that is part of a computer system.
  • Method 300 then comprises calculating 302 by the communication device, a quantity of the protein in the sample based on the visual indication in the image data.
  • the quantity of the protein calculated based on the visual indication in the image data may be above a determined threshold, such as the limit of the detection (LOD) of sampling device, for example.
  • LOD limit of the detection
  • the visual indication is given by the test area 206 of the absorbent material in the sampling device.
  • the camera may capture the test area 206 and the blank area 211 of the absorbent material in the sampling device.
  • the communication device calculates 302 a quantity of the protein in the sample based on the visual indication in the image data.
  • This step may comprise calculating an intensity ratio in the image data by comparing a first intensity of a test area 206 on the absorbent material with a second intensity of the blank area 211.
  • the blank area 211 corresponds to an area of the absorbent material other than the test area 206.
  • the second intensity may be determined using the control area 207 as in some scenarios, it is advantageous to take the measurement from the control area 207 instead of the white background of the blank area 211.
  • the quantity of the protein in the sample is based on the intensity ratio calculated by the processor of the communication device. That is, the processor calculates the intensity ratio using a mathematical operation using the first and second intensities as input and the intensity ratio as output. For example, the processor calculates the intensity ratio by dividing the first intensity by the second intensity.
  • Calculating 302 the intensity ratio may also further comprise creating calibration curves using multiple calibration samples by the processor of the communication device.
  • the calibration samples contain known concentrations of the protein.
  • a calibration curve also known as a standard curve, is a general method for determining the concentration of a substance in an unknown sample by comparing the unknown to a set of standard samples of known concentration.
  • the calibration curves may be indicative of a linear regression model or a line of best fit.
  • the processor of the communication device creates a calibration curve using image data of a sampling device containing a calibration sample containing a known concentration or quantity of the protein, captured by a camera of the communication device.
  • the processor calculates the intensity ratio in the image data by comparing a first intensity of a test area 206 on the absorbent material with a second intensity of the blank area 211, which corresponds to an area of the absorbent material other than the test area. This intensity ratio with the known concentration is used as a data point to create the calibration curve. This process is repeated using another sampling device containing a calibration sampling containing a known concentration of the protein that is different from the first calibration sample.
  • the processor may then apply this linear equation to the intensity ratio calculated from image data to determine an unknown quantity of the protein in the sample.
  • variable y in the linear equation may be the quantity of the protein in the sample and variable x may be the intensity ratio.
  • the constants m and c are calculated during the process of linear regression and are calculated to minimise the error between the data points determined earlier and the prediction of the quantity based on the linear equation. It is noted that the more data points created, i.e., the more calibration samples used with different concentrations or quantities of the protein, the more accurate the calibration curve will be.
  • Adding data points to a calibration curve may be manually added by a user.
  • the calibration curves may also be pre-set by the developer of the communication device, enabling the user to only take an image of the sample and use a pre-set calibration curve to estimate the quantity of concentration of the protein in the sample.
  • a calibration curve may be created by the processor of the communication device for one particular type of sampling device.
  • the processor creates a calibration curve for a group of sampling devices. For example, if the sampling device was a LFA manufactured to detected the presence of a protein relating to a peanut allergen, the processor may create a calibration curve for one particular brand of LFA that detects this protein, or the processor may use a calibration curve for all LFAs that detect this protein. The processor may then store one of the calibration curves in the communication device for use with sampling devices of multiple predefined types.
  • Method 300 then comprises sending 303 the image data from the communication device to a server device through the internet or any other means of remote communication of data.
  • the server device may be remotely located from the communication device.
  • the server device may also comprise a processor as well as additional modules, that are configured to store the image data on the server device or further communicate the image data.
  • the processor of the server device may be additionally configured to perform one or more of the methods described herein. For example, the processor of the server device may calculate an intensity ratio of image data, create the one or more calibration curves and determine the quantity of the protein in the sample based on the image data.
  • the processor of the server device confirms 304 the identity of the sampling device using the data indicative of the machine readable unique identifier in the image data.
  • the machine readable unique identifier may encode information that, when decoded, confirms the identity of the sampling device.
  • the machine readable unique identifier may also be used to look up information, such as the identity of the sampling device, manufacturer, date/time of manufacture, protein that is detectable by the sampling device, using a look table, such as a database.
  • the machine readable unique identifier may be used to determine whether the sampling device captured in the image data is the same sampling device for which LC-MS analysis is performed on in a subsequent step.
  • method 300 comprises confirming 305 the quantity of the protein in the sample based on the visual indication in the image data by using the internal standard in liquid chromatography mass spectrometry (LC-MS) analysis of the sample extracted from the sampling device.
  • the visual indication of the sampling device may be used as a primarily indication of the presence of the protein in the sample
  • capturing 301 image data of the sampling device and calculating 302 the quantity of the protein in the sample based on the visual indication in the image data provides a primarily quantification of the protein. The preliminary quantification of the protein is, thus, able to be verified through LC-MS analysis.
  • the quantity of the protein in the sample may be lower than the limit of detection (LOD) of the sampling device.
  • LOD limit of detection
  • a quantity that is lower than the LOD would not show a perceivable visual indication of the protein on the sampling device, therefore the protein would be undetected by visual inspection.
  • LC-MS analysis can detect quantities of protein lower than the LOD of many sampling devices. Therefore, the LC-MS analysis may additionally detect the protein in the sample from the sampling device.
  • the calibration curves used to determine the quantity of a protein in a sample may also be updated using the results of the LC-MS analysis, at the corresponding intensity ratio determined from the image data. In other words, it is possible to update the calibration curves at the intensity ratio in the image data with the quantity of the sample determined using the LC-MS analysis. As a result, many data points would be available to create accurate calibration curves, as these curves can be continually updated. This is an advantage when many communication devices are in communication with the server device, as each communication device sends image data or calculates an intensity ratio to determine a data point for a calibration curve.
  • the data points created from a communication device may be stored on the communication device or communicated to the server device and stored on the server device. The stored data points may be later used to create calibration curves.
  • Fig. 3b illustrates a method 350 for detecting a protein in a sample.
  • This method may comprise determining the level of the protein above or below a determined threshold.
  • the determined threshold may be the limit of detection (LOD) of the sampling device, for example.
  • Method 350 may be performed in conjunction with or instead of method 100 or 300. It is noted that method 300 of Fig. 3a and method 350 of Fig. 3b share similarities and even share similar steps. As such, aspects of method 350, such as the communication device, image data, machine -readable unique identifier, server device, LC-MS analysis that were described above for method 300, are equivalent for method 350.
  • Method 350 first comprises capturing 351 image data of a sampling device containing the sample with a camera of a communication device.
  • Method 350 then comprises processing 352 by the communication device, the image data to detect the visual indication in the image data to thereby detect the protein in the sample using the visual indication in the image data.
  • the visual indication of the protein in the sample may not be visible by the naked-eye due to the quantity of the protein being below a certain threshold or due to ambient lightning and other interfering lightning conditions.
  • processing 352 the image may comprise correcting the image data using a correction ratio calculated based on an average of the first intensity and an average of the second intensity. For example, the processor calculates the correction ratio by dividing the average of the first intensity by the average of second intensity. Similar to method 300 of Fig. 3a, the first intensity is indicative of the test area on the absorbent material and second intensity is indicative of an area of the absorbent material other than the test area (i.e., either the control area 207 or the blank area 211).
  • correcting the image data may comprise detecting the visual indication of the protein in the sample in the image data to thereby detect 353 the protein in the sample from the image data. Detecting 353 the protein in the sample from the image data provides a preliminary detection of the protein in the sample, which can be later confirmed. It is noted that the visual indication does not necessary mean visual indication by human interpretation. Visual indication may also refer to visual indication by machine interpretation, in such a way, that the visual indication is machine -readable.
  • Method 350 then comprises sending 354 the image data from the communication device to a server device through the internet or any other means of remote communication of data.
  • the processor of the server device confirms 355 the identity of the sampling device using the data indicative of the machine readable unique identifier in the image data.
  • Step 354 and 355 of method 350 are equivalent to steps 303 and 304 of method 300.
  • method 350 comprises determining 356 a quantity of the protein in the sample using the internal standard in liquid chromatography mass spectrometry (LC-MS) analysis of the sample extracted from the sampling device.
  • LC-MS liquid chromatography mass spectrometry
  • detecting 353 the protein in the sample using the visual indication in the image data gives a preliminary indication of the presence of the protein in the sample. This is an advantage in situations where quick detection of the protein in the sample is desired, but the quantity of the protein is not necessarily required, or is not required at the time that the sample is collected using the sampling device.
  • step 356 of method 350 may comprise, in response to confirming 355 the identity of the sampling device, confirming detection of the protein in the sample based on the visual indication in the image data using liquid chromatography mass spectrometry (LC-MS) analysis of the sample extracted from the sampling device.
  • the visual indication of the sampling device may be used as a preliminary indication of the presence of the protein in the sample.
  • capturing 351 image data of the sampling, processing 352 image data to detect the visual indication in the image data and detecting 353 the protein in the sample using the visual indication in the image data provides a preliminary detection of the protein.
  • the preliminary detection of the protein is, thus, able to be verified through LC-MS analysis.
  • method 350 may additionally comprise step 302 of method 300. That is, method 350 may further comprise calculating a quantity of the protein in the sample based on the visual indication in the image data. This quantity may also be confirmed by the LC-MS analysis.
  • the LC-MS analysis is able to detect the quantity of the protein below or above a threshold, such as, for example, the limit of detection (LOD) of the sampling device or an acceptable limit of protein for food safety.
  • a threshold such as, for example, the limit of detection (LOD) of the sampling device or an acceptable limit of protein for food safety.
  • LOD limit of detection
  • the LC-MS analysis may also help improve the detection of the protein in the sample by improving the processing, by the communication device, of the image data in step 352.
  • the results of the LC-MS analysis may be used to update the calibration curves used by the communication device.
  • any one of the previously described methods may further comprise determining peptide sequence information of the proteins captured by the sampling device using the LC-MS analysis.
  • the sample is extracted from the sampling device with an extraction solution.
  • a digestion solution may be ran through the sample.
  • the digestion solution is used to digest the protein(s) in the sample and into their constitute peptides.
  • the solution with the constitute peptides may then travel through the liquid chromatography (LC) system, which separates the peptide in the mixture according to their adherence to a solid adsorbent material within the column of the LC system.
  • LC liquid chromatography
  • This provides the identity of the peptides present in the sample, and may provide the peptide sequence information.
  • the peptide sequence information and or the information of the mass to charge ratio of the peptide and it’s fragments (in MS/MS mode) can then be used to identify and/or quantify the protein(s) that were present in the sample.
  • any one of the previously described methods may also further comprise detecting the presence of a second or additional proteins using the LC-MS analysis to verify a purported origin of the sample.
  • the sample may contain one or more proteins in addition to the protein of interest. The detection or lack of detection of expected proteins in the sample may confirm where the sample has been collected from.
  • the method may further comprise detecting a secondary protein, such as a tomato protein. Detecting the presence of the tomato protein would indicate that the sample has be collected from tomato soup.
  • a user transports, delivers or sends a sampling device that contains a sample and claims the sample was collected from tomato soup this claim can be verified by detecting the presence of a tomato protein in the sampling device via LC-MS analysis.
  • Fig. 4 illustrates an example system 400 for performing one or more of the methods described herein.
  • system 400 may be used to perform method 100 illustrated in Fig. 1.
  • system 400 may be used to perform method 300 illustrated in Fig. 3a.
  • system 400 may be used to perform method 350 illustrated in Fig. 3b.
  • references to method 300 and method 350, as well as the steps of method 300 and the steps of method 350 are used to describe system 400, however system 400 is not limited to performing method 300 or method 350.
  • System 400 comprises a sampling device 401 and a communication device 402.
  • Communication device 402 may comprise a processor 403 connected to a program memory 404 and a data memory 405.
  • the program memory 404 is a non-transitory computer readable medium, such as a hard drive, a solid state disk or CD-ROM.
  • Software that is, an executable program stored on program memory 404 causes the processor 403 to perform some steps of the method in Fig. 3a, that is, processor 403 captures 301 image data of the sampling device, calculates 302 a quantity of the protein in the same based on the visual indication in the image and sends 303 the image data to a server device.
  • Software may also perform some of the steps of method 350 in Fig. 3b, that is, processor 403 captures 351 image data of the sampling device, process 352 image data to detect the visual indication in the image data, detects 353 the protein in the sample using the visual indication in the image data and sends 354 the image data to a server device.
  • the data memory 405 may store image data and retrieve the image data for later use.
  • the data memory 405 may also store data relating to the quantity of the protein in the sample, as well as data relating to the calculation of this quantity including, but not limited to, the intensity ratio of image data, the correction ratio of image data, data points created from image data of calibration samples and calibration curves.
  • the image data may also be indicative of a two-dimensional image, which may be stored on data memory 405 as Joint Photographic Experts Group (JPEG) format, RAW image format or a similar/equivalent image format, for example.
  • JPEG Joint Photographic Experts Group
  • the processor 403 may receive data through all these interfaces, which includes memory access of volatile memory, such as cache or RAM, or non-volatile memory, such as an optical disk drive, hard disk drive, storage server or cloud storage.
  • the communication device 402 further comprises a camera 406 that is in communication with the processor 403 via the input/output (I/O) port 407.
  • camera 406 is integrally contained in the communication device 402, despite being drawn as disjointed entities in Fig. 4.
  • Camera 406 captures image data of the sampling device 401, which is then communicated to the processor 403 via the I/O port 407.
  • processor 403 may then store the image data on data memory 405, such as on RAM or a processor register.
  • processor 403 requests the data from the data memory 405, such as by providing a read signal together with a memory address.
  • the data memory 405 provides the data as a voltage signal on a physical bit line and the processor 403 receives the image data as the input image via a memory interface.
  • Steps 301-303 of Fig. 3a and steps 351-354 of Fig. 3b are to be understood as a blueprint for the software program and may be implemented step-by-step, such that each step is represented by a function in a programming language, such as C++ or Java.
  • the resulting source code is then compiled and stored as computer executable instructions on program memory 404.
  • the communication device 402 may further comprise a communication port 408, allowing the communication device 402 to establish a communication with a server device 409.
  • the communication port 408 may have the same input and output functionally as I/O port 407.
  • the I/O port 407 and communication port 408 may be the same port, although they are drawn as distinct entities in Fig. 4.
  • communication port 408 may be a wireless transmitter and/or receiver, that is configured to send and receive data, such as the image data, with the server device 409.
  • server device 409 may comprise a processor, data memory, program memory, and one or more I/O or communication ports. In this sense, server device 409 may have similar functionally to the communication device 402, and may be configured to perform similar methods and/or processes to that of the communication device 402.
  • the network of communication devices may also communicate directly with one another via the internet, any other means of wireless communication or wired connections.
  • the same calibration curve may be communicated, over a communication network, from the server device 409 to the multiple communication devices.
  • Each of the multiple communication devices may then use the calibration curve to determine the quantity of a protein in a sample from image data that each of the multiple communication devices capture.
  • the server device 409 may retrieve and store data such as, but not limited to, image data, calibration curves and spectrum produced during LC-MS analysis. Additionally, the server device 409 may then communicate this data back to the communication device 402 and/or a computer 410.
  • the image data may be received from a source external to system 400, such as another system located remotely to system 400, which may be in communication with the server device 409.
  • the remote system may be located within the same facility as system 400 or may be completely remote from system 400.
  • the image data may be stored in the server device 409 as Joint Photographic Experts Group (JPEG) format, RAW image format or a similar/equivalent image format.
  • JPEG Joint Photographic Experts Group
  • processor 403 receives the image data via the input/output port 308 and performs aspects of method 300 of method 350 on the image data. After processor 403 performs aspects of method 300 or method 350 on the image data, the image data, the quantity of the protein in the sample and/or the detection of the protein in the sample determined by processor 403 may be communicated to the server device 409. The server device 409 may then communicate the image data and/or the quantity of the protein in the sample to an external system or store them.
  • Server device 409 may be configured to create and store calibration curves used to calculate the quantity and detect the protein above a set threshold level in the sample using the visual indication of the protein in image data.
  • the communication device 402 may capture multiple instances of image data of sampling devices with different calibration samples and communicate the multiple instances of image data to server device 409.
  • Server device 409 may then calculate an intensity ratio for each instance of image data by comparing a first intensity of a test area on the absorbent material with a second intensity of an area of the absorbent material other than the test area.
  • Each intensity ratio and the known concentration of the protein in the calibration samples can then be used by the server device 409 to create the one or more calibration curves.
  • These calibration curves can then be stored on the server device 409 and later retrieved and/or communicated to the communication device 402 to calculate the unknown quantity of a protein in a sample.
  • System 400 may also comprise a computer 410 that may be in communication with a liquid chromatography mass spectrometry (LC-MS) system 411.
  • LC-MS liquid chromatography mass spectrometry
  • the computer 410 and the LC-MS system 411 may be a single entity or may be multiple entities, as depicted in Fig. 4.
  • Computer 410 may comprise a processor, a data memory module, a program memory module and one or more I/O or communication ports, similar to the communication device 402.
  • Computer 410 may be configured to provide instructions and/or control the operation of the LC-MS system 411 and may be further configured to retrieve and/or receive data from the LC-MS system 411.
  • Computer 410 may then process the data communicated from the LC-MS system 411, which may include creating a mass spectrum of the sample extracted from the sampling device and/or determining the quantity of the protein in the sample based on the visual indication in the image data.
  • a sample may be collected from a first location using the sampling device 401 and the LC-MS analysis may be performed by the LC-MS system 411 at a second location remote from the first location.
  • the sampling device 401 that contains the sample collected from the first location may be transported from the first location to the second location, as indicated by path 412.
  • the sampling device 401 may be used as a sample carrier, but may also be used as a preliminary means of detection of the protein in the sample, due to the visual indication provided by the sampling device 401. This may be also be referred to as “on-site testing”.
  • the LC-MS system 411 may be used to confirm the detection of the protein in the sample based on the visual indication, which may be referred to as “confirmatory testing”. Further, when integrated with the communication device 402 and the server device 409, the communication device 402 may calculate a quantity of the protein in the sample based on the visual indication. Thus, the communication device 402 may provide a preliminary quantity/detection of the protein in the sample. The LC-MS system 411 may then analyse the sample by extracting it from the sampling device 401 and the results from the analysis may be communicated to computer 410. Computer 410 may then calculate the quantity of the protein in the sample based on the LC-MS analysis, thereby confirming the preliminary quantity/detection of the protein in the sample based on the visual indication.
  • Computer 410 may then communicate the results of the LC-MS analysis, as performed by the LC-MS system 411, to the server device 409.
  • the server device 409 may then communicate these results to the communication device 402.
  • the communication device 402 may display the quantity of the protein calculated by processor 403, as well as the quantity determined by the LC-MS system 411 by LC-MS analysis.
  • Software may provide a user interface presented to the user on communication device 402 and/or computer 410.
  • the user interface is configured to accept input (via buttons or text fields etc.) from the user, via a touch screen or a device attached to communication device 402 and/or computer 410 such as a keyboard or computer mouse.
  • These devices may also include a touchpad, an externally connected touchscreen, a joystick, a button, and a dial.
  • communication device 402 and/or computer 410 may display multiple instances of image data, and the user may choose one of the multiple instances of image data for method 300 to be performed on, by processor 403. The user may choose one of the multiple instances of image data by interacting with the touch screen or inputting the selection with a keyboard or computer mouse.
  • the processor 403 may receive or send data, such as image data, from data memory 405 as well as from the I/O port 407 and/or the communication port 408.
  • the processor 403 sends image data from the communication device 402 to server device 409 via communications port 408, such as by using a Wi-Fi network according to IEEE 802. 11.
  • the Wi-Fi network may be a decentralised ad-hoc network, such that no dedicated management infrastructure, such as a router, is required or a centralised network with a router or access point managing the network.
  • Communication between server device 409 and computer 410 may similarly established using a Wi-Fi network according to IEEE 802.11.
  • System 400 may further be implemented within a cloud computing environment, such as a managed group of interconnected servers hosting a dynamic number of virtual machines.
  • communication port 408 is shown as single entity, it is to be understood that any kind of data port may be used to receive data, such as a network connection, a memory interface, a pin of the chip package of processor 403, or logical ports, such as IP sockets or parameters of functions stored on program memory 404 and executed by processor 403.
  • the parameters of functions may be stored on data memory 405 and may be handled by-value or by-reference, that is, as a pointer, in the source code.
  • Fig. 4 is one example of a configuration of system 400. However, system 400 is not strictly limited to this configuration and this may be one possible embodiment of system 400.
  • computer 410 may be in direct communication with the communication device 402.
  • Ratio-metric approaches and shutter time optimisation were conducted using gold nanoparticles (GNPs) dropped onto filter paper at various concentrations. GNPs were synthesised following the Turkevich method. Calibration curves were photographed as both RAW and JPEG images using an Android (Galaxy S21 Ultra) with the white balance fixed at 5000, ISO at 100 using 1/30, 1/45 and 1/60 shutter times and backlit conditions. All photographs were backlit to reduce illumination variation. From this, ImageJ (VI.53k, https://imagej.nih.gov/ij/) was used to extract RGB values from JPEG files and intensity values from RAW files.
  • Readings were taken from the test area, the control area and the blank area, corresponding to the white section of the absorbent material just below the test area. These measures were used to compare the performance of both uncorrected and ratio corrected values using either the control area or the white background as the denominator.
  • the smartphone cameras were operated in Pro-mode (Android) or using the ProCam application for the iPhone (model S6) to enable full control over the camera settings. The shutter times 1/60, 1/45 and 1/30 were tried in optimisation experiments.
  • Peanut calibration curves were constructed by serial 2-fold dilutions from 2000 pg mL-1 to 0.49 pg mL-1 in phosphate buffered saline (PBS; pH 7.2).
  • PBS phosphate buffered saline
  • a calibration curve was constructed by serial 2-fold dilutions from 8000 pg mL-1 to 0.98 pg mL-1 in PBS.
  • the LFA assay protocols provided by the manufacturers (Romer and 3M) were followed for all experiments. The results were photographed using the fixed camera settings that were previously optimised using the GNP spots.
  • LC vials Flowthrough of the wash and collection steps were pooled and lyophilised in a speed vac. Lyophilised peptides were solubilised in 50 pL of acidified water spiked with iRT (0.1% FA, 0.05 iRT picomol pL-1) and transferred to LC vials and stored in a freezer pre-analysis. A scheduled multiple reaction monitoring (sMRM) method for gluten and peanut quantification was followed for the analysis of the sample extracts. The samples (8 pL per injection) were analysed on a SCIEX 6500 QTRAP mass spectrometer coupled to a Shimadzu Nexera UHPLC system.
  • sMRM multiple reaction monitoring
  • An Android app has been built to take images of the absorbent material.
  • the app was built using the Android's Camera2 API (https://developer.android.com/training/camera2) source code.
  • the macro camera was selected to gain better focus for the close proximity targets.
  • the developed app allows the user to zoom into the test and the control area and then move the two detection zones manually to select the test-strip areas, such as the test area and the control area, that need to be read to calculate a ratiometric value and the average colour/intensity result is displayed on the screen.
  • the ratiometric value can then be interpolated in the app using a calibration curve function that was created using a set of LFAs photographed at known concentrations.
  • the ISO, shutter time and white balance can be fixed in the app. Images can be saved as JPEG or DNG (RAW) images.
  • the smartphone application is software that, when installed on a communication device, such as an Android smartphone, and executed by the communication device, causes the communication device to, for example, capture image data of the sampling device with a camera; calculate an intensity ratio in the image data by comparing a first intensity of a test area on the absorbent material with a second intensity of an area of the absorbent material other than the test area; wherein the test area provides the visual indication of the protein in the sample; and calculate a quantity of the protein present in the sample based on the intensity ratio.
  • a communication device such as an Android smartphone
  • ImageJ VI.53k was used to extract RGB and RAW intensity values from captured images.
  • data from photos were linearised using the using the DCRaw reader plugin (v. 9.27x ) in “totally RAW” document mode with a 16-bit output.
  • Spectra analysis and method development were conducted using Skyline v21.1.0.278.
  • GraphPad 8.0.1 and Inkscape 1.1.1 were used to prepare and assemble statistical analysis and figures.
  • a calibration curve of various GNP concentrations was dropped on filter paper and photographed using an Android phone in both RAW and JPEG image formats to optimise the camera settings and test the performance of the ratio metric ambient light correction. Shutter times of 1/30, 1/45 and 1/60 were used with all other camera settings kept constant.
  • R, G, B, average grayscale, and weighted grayscale measurements were extracted from 8-bit JPEG images and intensity values were extracted from 16-bit RAW images. These values were fitted using a four- parameter log(dose)-response curve.
  • Fig. 5 illustrates different calibration curves for relative gold nanoparticle (GNP) concentration created using image data from a communication device.
  • Graphs A-B show calibration curves for relative GNP concentrations from uncorrected intensity values of JPEG colour channels (red, blue, green), average grayscale and weighted grayscale values (A) and raw intensity values (B).
  • Graphs C-G show calibration curves for relative GNP concentration values from ratio (signal/white background) corrected JPEG colour and greyscale channels.
  • Ratio corrected values where the average extracted intensity of the test area was divided by the average extracted intensity of the blank area of the absorbent material (between the test and control area), were mainly shown to have similar or slightly lower LOD and IC50 values.
  • the method for detecting the protein in the sample and/or determining the quantity of the protein in the sample further comprises correcting the image data using a correction ratio calculated based on an average of the first intensity and an average of the second intensity.
  • the slightly lower LOD and IC50 values may be caused by a reduction in background illumination noise.
  • the correction ratio may also be used to qualitatively detect the protein in the sample from the LFA (sampling device).
  • LFA sampling device
  • detecting the protein using the LFA relies on the visual indication, which is read by the naked-eye.
  • illumination noise and other lighting factor may affect the ability for a user to determine the presence of the visual indication, the protein may not always be detected by a naked-eye reading. Therefore, by capturing image data of the sampling device containing the sample, the communication device that captures the image data may calculate a correction ratio and correct the image data. The communication device may then be able to detect the visual indication of the sampling device, or a user may be able to see the visual indication after the correction. Therefore, any one of the method described herein may further comprises correcting the image data using a correction ratio and detecting the protein in the sample from the corrected image data.
  • ratio correction did have a negative effect on the fit (R2) of the calibration curve to the data across JPEG and RAW image formats. This negative effect was the least prominent in the RAW ratio corrected images.
  • ratio correction mitigates the impact of shutter time, as a proxy for light intensity, on the yielded intensity values as shown by the corrected ratio calibration curves falling over each other across the investigated shutter times (Fig. 5C-H) in comparison with the uncorrected calibration curves (Fig. 5A-B). Although this was observed to varying extents across the colour channels within JPEG images, this effect was most distinct in the ratio corrected RAW images (Fig. 5H). This suggests that raw images enable high quality corrections for light intensity changes within this straightforward ratio metric approach.
  • Fig. 6 illustrates calibration curves of GNP concentration and intensity in colour channels of JPEG and RAW images captured using Android and iPhone mobile phone models at 1/60 shutter time. Inter-phone variation has an effect on LFA quantification using ratio-metric measurements for the various tested colour channels and intensity measurements.
  • the ratio-metric approach improves calibration curve overlap compared to uncorrected calibration curves. Clear differences in the amount of calibration curve overlap and shape can be observed for the tested image and colour/intensity formats applied.
  • Ratio-corrected raw intensity and blue channel JPEG calibration curves enabled the closest fits between the two phone models compared.
  • ratio corrections decreased the R2 of the Android calibration curves but improved or did not affect the fit of the iPhone calibration curves compared to the R2 values obtained for the uncorrected data in the JPEG image format. This shows that ratio correction varied in effectiveness depending on the phone model when the JPEG format was used. For the raw images, R2 values were excellent for both phone models and did not differ between uncorrected and ratio corrected calibration curves.
  • the raw intensity values also led to particularly low relative standard deviations (RSDs) for both phones (iPhone 1.05 ⁇ 0.51%; Android 1.41 ⁇ 0.42%); was the only measurement type that enabled the detection of GNPs at a concentration below a relative value of one; and, was the only measurement type that showed no plateau in the higher concentration range of the GNPs for both phone models.
  • ratio corrected raw values were used from here on as these were considered to outperform all JPEG colour channel and intensity measurements in terms of ambient-light variation robustness, inter-phone variation, dynamic range, precision and sensitivity.
  • Other techniques aiming to overcome this limitation such as the use of a colour chart added to each image or a camera calibration approach can be incorporated to further reduce the issue of inter-phone variation.
  • Calibration curves were constructed using ratio metric raw intensity values for two commercial peanut LFAs (Romer and 3M) and a gluten LFA (3M) using the Android and the iPhone (Fig. 7; Table 2).
  • the iPhone underperformed compared to the android in terms of R2 values and the A ratio observed between the maximum and minimum signal across all investigated assays. Nonetheless, high R2 values (0.94 ⁇ R2 ⁇ 1.00) and exceptionally low intra-day repeatability Relative Standard Deviations (RSDs;between 1.0 and 7.0%) were obtained for all calibration curves.
  • Table 2 Analytical parameters of the calibration curves shown in Fig. 7.
  • Fig. 7 illustrates smartphone based quantification of commercial lateral flow assays.
  • the iPhone calibration curves of the Romer and 3M assays show excellent overlap while the Android calibration curves do not (Fig. 7A). This indicates that assay specific calibration do not need to be corrected for commercial assay variation for certain phone models with this ratio-metric approach.
  • the inter-day repeatability was also tested for the peanut LFA assays with new samples tested one month after the calibration curve generation.
  • the developed Android app allows users to take images of test strips at the determined optimal camera settings and calculate average colour/intensity values from JPEG and raw images using the RGB and greyscale colour/intensity algorithms.
  • the smartphone can be placed directly above the image as long as there is an angled light source that eliminates shade formation on the images.
  • Fig. 8 illustrates a smartphone application for colour quantification. Captured images can be processed for quantification and the data can be stored in the cloud for later use.
  • Fig. 8 shows the lateral flow assay prior to zooming in on the test line. The red box indicates the zoom area. The right image shows the zoomed in area with the boxes used to select the test strip area and white area used for ratio metric value calculation. Average colour area values are indicated.
  • the captured images can be stored in a cloud database and can also be processed for quantification and stored for later use. Two individual blocks can be dragged by the user to the relevant test strip/background areas and the average colour results are displayed on the screen (Fig. 8).
  • the Android app can also analyse images taken from other devices. Colour analysis of the images may also be adapted using the DCraw algorithm as used in the ImageJ plugin, given the discovered substantial benefit of using raw intensity values.
  • Fig. 9 illustrates optimisation of the LC-MS extraction protocol.
  • Graph B shows the average coefficient of variation (%CV or simply CV) for sampling and wicking pad extracts of the top ten identified peanut peptides.
  • Peanut extracts from the sampling pad of multiple sampling devices, wicking pads and test area of the LFAs were analysed by LC-MS to determine which section of the LFA is best suited for LC-MS analysis.
  • the top ten best performing peptides were then selected for further assay optimisation. For this selection a cut-off was used whereby the signal intensity of the wicking pad and/or the sampling pad needed to have an ion count of at least 1* 10 6 a.u.
  • sampling pad extracts were considerably higher than the peak areas obtained for the wicking pad for all selected peptides (Fig. 9A). Thus, the sampling pads likely contained the highest peanut protein concentration across the multiple different sampling devices.
  • the peak area CVs for the sampling pads were also considerably lower as the wicking pad CVs for nine of the 10 peptides tested with various CVs well below 10% (Fig. 9B).
  • Sampling pad CV’s may have been best performing as the composition of protein extracts from wicking pads were more sensitive to unintentional variation in the extraction and LFA assay protocol. Such variation would cause larger changes to the CVs in the wicking pad extracts due to the lower protein concentration in these extracts. Thus, sampling pads were used for the remainder of extractions.
  • LC-MS performance was tested for the quantification of peanut and gluten protein directly from the sampling pads of the commercial LFAs with the quantitative transitions of the best performing peptides for each assay (TANDLNLLILR for peanut and LEGSDALSTR for gluten determination). It is noted that each letter of the peptide represents one of the 20 common amino acids found in proteins.
  • Fig. 10 illustrates the direct quantification of allergenic peptides from LFAs by LC-MS.
  • Graph D shows RSDs (n>18) calculated forthe calibration curves shown in Figs. 10A-C.
  • TANDLNLLILR is a unique identifier for cultivated peanut and wild peanut protein (both of which carry the peanut allergens).
  • any one of the method described herein may additional comprise detecting a false negative or a false positive by the sampling device (LFA) using the LC-MS analysis to detect the protein below a limit of detection of the sampling device.
  • LFA sampling device
  • Ratio-metric ambient light corrections also performed particularly well for the raw image format, and it was shown that such corrections can be successfully used to avoid the use of additional light sources or a light-shielding box. Instead, a simple picture with fixed camera parameters can be taken and used for the robust quantification of test-strips. Moreover, striking calibration curve overlap was observed for two different LFA assays for peanut quantification when the same phone model was used suggesting it may be possible to construct general calibration curves that can be applicable for multiple commercial assays detecting the same target if a given phone model is used.

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Abstract

L'invention se rapporte à la détection d'une protéine dans un échantillon. L'invention porte sur un système de détection d'une protéine dans un échantillon, comprenant un dispositif d'échantillonnage configuré pour recevoir l'échantillon, le dispositif d'échantillonnage comprenant un matériau absorbant avec une zone de test configurée pour fournir une indication visuelle de la protéine dans l'échantillon. Le système comprend en outre un appareil de prise de vues configuré pour capturer des données d'image du dispositif d'échantillonnage, les données d'image capturant l'indication visuelle, et un système de spectrométrie de masse par chromatographie en phase liquide (LC-MS) configuré pour effectuer une analyse LC-MS en vue de détecter la protéine dans l'échantillon extrait du dispositif d'échantillonnage, l'analyse LC-MS confirmant la détection de la protéine comme indiqué visuellement par les données d'image.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160305954A1 (en) * 2015-04-17 2016-10-20 Kenneth Davin Fine Diagnostic testing for immunologic food sensitivity
WO2016182905A1 (fr) * 2015-05-08 2016-11-17 Alpha Diagnostic International, Inc. Bandes pour le transfert quantitatif d'échantillons biochimiques
WO2021195633A1 (fr) * 2020-03-27 2021-09-30 Marshall Venture Partners LLC Systèmes et procédés de collecte et de test d'échantillon de fluide à l'aide d'un ensemble écouvillon à bandes de test immunochromatographique intégrées
WO2022032184A1 (fr) * 2020-08-05 2022-02-10 The Administrators Of The Tulane Educational Fund Procédé de détection de tb dans des échantillons de fluide corporel

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160305954A1 (en) * 2015-04-17 2016-10-20 Kenneth Davin Fine Diagnostic testing for immunologic food sensitivity
WO2016182905A1 (fr) * 2015-05-08 2016-11-17 Alpha Diagnostic International, Inc. Bandes pour le transfert quantitatif d'échantillons biochimiques
WO2021195633A1 (fr) * 2020-03-27 2021-09-30 Marshall Venture Partners LLC Systèmes et procédés de collecte et de test d'échantillon de fluide à l'aide d'un ensemble écouvillon à bandes de test immunochromatographique intégrées
WO2022032184A1 (fr) * 2020-08-05 2022-02-10 The Administrators Of The Tulane Educational Fund Procédé de détection de tb dans des échantillons de fluide corporel

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Title
BLUEMLEIN KATHARINA, RALSER MARKUS: "Monitoring protein expression in whole-cell extracts by targeted label- and standard-free LC-MS/MS", NATURE PROTOCOLS, NATURE PUBLISHING GROUP, GB, vol. 6, no. 6, 1 June 2011 (2011-06-01), GB , pages 859 - 869, XP093127037, ISSN: 1754-2189, DOI: 10.1038/nprot.2011.333 *
NELIS JOOST L. D., MODDEJONGEN SARAH, GUAN XINLONG, ANDERSON ALISHA, COLGRAVE MICHELLE L., BROADBENT JAMES A.: "Secure Food-Allergen Determination by Combining Smartphone-Based Raw Image Analyses and Liquid Chromatography–Mass Spectrometry for the Quantification of Proteins Contained in Lateral Flow Assays", ANALYTICAL CHEMISTRY, AMERICAN CHEMICAL SOCIETY, US, vol. 94, no. 49, 13 December 2022 (2022-12-13), US , pages 17046 - 17054, XP093127039, ISSN: 0003-2700, DOI: 10.1021/acs.analchem.2c03000 *

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